Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations32
Missing cells21
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory108.1 B

Variable types

Categorical4
Text1
Numeric8

Alerts

am is highly overall correlated with drat and 2 other fieldsHigh correlation
carb is highly overall correlated with disp and 5 other fieldsHigh correlation
cyl is highly overall correlated with disp and 5 other fieldsHigh correlation
disp is highly overall correlated with carb and 8 other fieldsHigh correlation
drat is highly overall correlated with am and 4 other fieldsHigh correlation
gear is highly overall correlated with am and 5 other fieldsHigh correlation
hp is highly overall correlated with carb and 8 other fieldsHigh correlation
mpg is highly overall correlated with carb and 7 other fieldsHigh correlation
power is highly overall correlated with carb and 7 other fieldsHigh correlation
qsec is highly overall correlated with carb and 4 other fieldsHigh correlation
vs is highly overall correlated with carb and 8 other fieldsHigh correlation
wt is highly overall correlated with am and 7 other fieldsHigh correlation
model has 4 (12.5%) missing values Missing
mpg has 1 (3.1%) missing values Missing
cyl has 1 (3.1%) missing values Missing
disp has 3 (9.4%) missing values Missing
hp has 2 (6.2%) missing values Missing
drat has 1 (3.1%) missing values Missing
wt has 1 (3.1%) missing values Missing
qsec has 4 (12.5%) missing values Missing
vs has 1 (3.1%) missing values Missing
am has 1 (3.1%) missing values Missing
power has 2 (6.2%) missing values Missing

Reproduction

Analysis started2025-02-17 06:42:02.724772
Analysis finished2025-02-17 06:42:20.560259
Duration17.84 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gear
Categorical

High correlation 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
3
15 
4
11 
5
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 15
46.9%
4 11
34.4%
5 5
 
15.6%
2 1
 
3.1%

Length

2025-02-17T12:12:20.669050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T12:12:20.740840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 15
46.9%
4 11
34.4%
5 5
 
15.6%
2 1
 
3.1%

Most occurring characters

ValueCountFrequency (%)
3 15
46.9%
4 11
34.4%
5 5
 
15.6%
2 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 15
46.9%
4 11
34.4%
5 5
 
15.6%
2 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 15
46.9%
4 11
34.4%
5 5
 
15.6%
2 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 15
46.9%
4 11
34.4%
5 5
 
15.6%
2 1
 
3.1%

model
Text

Missing 

Distinct28
Distinct (%)100.0%
Missing4
Missing (%)12.5%
Memory size388.0 B
2025-02-17T12:12:20.947622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length11.5
Min length5

Characters and Unicode

Total characters322
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st rowMaserati Bora
2nd rowPorsche 914-2
3rd rowFerrari Dino
4th rowLotus Europa
5th rowFord Pantera L
ValueCountFrequency (%)
merc 7
 
12.3%
mazda 2
 
3.5%
rx4 2
 
3.5%
fiat 2
 
3.5%
hornet 2
 
3.5%
dino 1
 
1.8%
porsche 1
 
1.8%
maserati 1
 
1.8%
europa 1
 
1.8%
bora 1
 
1.8%
Other values (37) 37
64.9%
2025-02-17T12:12:21.139900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
 
9.0%
r 27
 
8.4%
a 26
 
8.1%
e 22
 
6.8%
o 18
 
5.6%
t 16
 
5.0%
i 15
 
4.7%
n 13
 
4.0%
c 11
 
3.4%
M 11
 
3.4%
Other values (41) 134
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29
 
9.0%
r 27
 
8.4%
a 26
 
8.1%
e 22
 
6.8%
o 18
 
5.6%
t 16
 
5.0%
i 15
 
4.7%
n 13
 
4.0%
c 11
 
3.4%
M 11
 
3.4%
Other values (41) 134
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29
 
9.0%
r 27
 
8.4%
a 26
 
8.1%
e 22
 
6.8%
o 18
 
5.6%
t 16
 
5.0%
i 15
 
4.7%
n 13
 
4.0%
c 11
 
3.4%
M 11
 
3.4%
Other values (41) 134
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29
 
9.0%
r 27
 
8.4%
a 26
 
8.1%
e 22
 
6.8%
o 18
 
5.6%
t 16
 
5.0%
i 15
 
4.7%
n 13
 
4.0%
c 11
 
3.4%
M 11
 
3.4%
Other values (41) 134
41.6%

mpg
Real number (ℝ)

High correlation  Missing 

Distinct25
Distinct (%)80.6%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean20.048387
Minimum10.4
Maximum33.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:21.227250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.4
5-th percentile11.85
Q115.35
median19.2
Q322.8
95-th percentile31.4
Maximum33.9
Range23.5
Interquartile range (IQR)7.45

Descriptive statistics

Standard deviation6.1217574
Coefficient of variation (CV)0.30534912
Kurtosis-0.08752528
Mean20.048387
Median Absolute Deviation (MAD)3.7
Skewness0.68621288
Sum621.5
Variance37.475914
MonotonicityNot monotonic
2025-02-17T12:12:21.331191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
21 2
 
6.2%
30.4 2
 
6.2%
22.8 2
 
6.2%
15.2 2
 
6.2%
10.4 2
 
6.2%
19.2 2
 
6.2%
15.8 1
 
3.1%
15 1
 
3.1%
26 1
 
3.1%
19.7 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
10.4 2
6.2%
13.3 1
3.1%
14.3 1
3.1%
14.7 1
3.1%
15 1
3.1%
15.2 2
6.2%
15.5 1
3.1%
15.8 1
3.1%
16.4 1
3.1%
17.3 1
3.1%
ValueCountFrequency (%)
33.9 1
3.1%
32.4 1
3.1%
30.4 2
6.2%
27.3 1
3.1%
26 1
3.1%
24.4 1
3.1%
22.8 2
6.2%
21.5 1
3.1%
21.4 1
3.1%
21 2
6.2%

cyl
Categorical

High correlation  Missing 

Distinct3
Distinct (%)9.7%
Missing1
Missing (%)3.1%
Memory size388.0 B
8.0
14 
4.0
10 
6.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row4.0
3rd row6.0
4th row4.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 14
43.8%
4.0 10
31.2%
6.0 7
21.9%
(Missing) 1
 
3.1%

Length

2025-02-17T12:12:21.473005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T12:12:21.556310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8.0 14
45.2%
4.0 10
32.3%
6.0 7
22.6%

Most occurring characters

ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
8 14
15.1%
4 10
 
10.8%
6 7
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
8 14
15.1%
4 10
 
10.8%
6 7
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
8 14
15.1%
4 10
 
10.8%
6 7
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
8 14
15.1%
4 10
 
10.8%
6 7
 
7.5%

disp
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)89.7%
Missing3
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean215.25862
Minimum4
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:21.647148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile72.94
Q1120.1
median167.6
Q3318
95-th percentile424
Maximum460
Range456
Interquartile range (IQR)197.9

Descriptive statistics

Standard deviation126.25587
Coefficient of variation (CV)0.58653107
Kurtosis-1.0510139
Mean215.25862
Median Absolute Deviation (MAD)90.4
Skewness0.37169333
Sum6242.5
Variance15940.545
MonotonicityNot monotonic
2025-02-17T12:12:21.767365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
167.6 2
 
6.2%
160 2
 
6.2%
360 2
 
6.2%
145 1
 
3.1%
351 1
 
3.1%
108 1
 
3.1%
75.7 1
 
3.1%
95.1 1
 
3.1%
120.3 1
 
3.1%
301 1
 
3.1%
Other values (16) 16
50.0%
(Missing) 3
 
9.4%
ValueCountFrequency (%)
4 1
3.1%
71.1 1
3.1%
75.7 1
3.1%
78.7 1
3.1%
79 1
3.1%
95.1 1
3.1%
108 1
3.1%
120.1 1
3.1%
120.3 1
3.1%
140.8 1
3.1%
ValueCountFrequency (%)
460 1
3.1%
440 1
3.1%
400 1
3.1%
360 2
6.2%
351 1
3.1%
350 1
3.1%
318 1
3.1%
304 1
3.1%
301 1
3.1%
275.8 1
3.1%

hp
Real number (ℝ)

High correlation  Missing 

Distinct20
Distinct (%)66.7%
Missing2
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean142.9
Minimum2
Maximum335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:21.852289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile56.5
Q194
median123
Q3180
95-th percentile255.45
Maximum335
Range333
Interquartile range (IQR)86

Descriptive statistics

Standard deviation73.984784
Coefficient of variation (CV)0.51773817
Kurtosis0.23644683
Mean142.9
Median Absolute Deviation (MAD)54.5
Skewness0.57089118
Sum4287
Variance5473.7483
MonotonicityNot monotonic
2025-02-17T12:12:21.962131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
175 3
 
9.4%
180 3
 
9.4%
110 3
 
9.4%
123 2
 
6.2%
245 2
 
6.2%
150 2
 
6.2%
66 2
 
6.2%
335 1
 
3.1%
264 1
 
3.1%
113 1
 
3.1%
Other values (10) 10
31.2%
(Missing) 2
 
6.2%
ValueCountFrequency (%)
2 1
 
3.1%
52 1
 
3.1%
62 1
 
3.1%
65 1
 
3.1%
66 2
6.2%
91 1
 
3.1%
93 1
 
3.1%
97 1
 
3.1%
105 1
 
3.1%
110 3
9.4%
ValueCountFrequency (%)
335 1
 
3.1%
264 1
 
3.1%
245 2
6.2%
230 1
 
3.1%
215 1
 
3.1%
180 3
9.4%
175 3
9.4%
150 2
6.2%
123 2
6.2%
113 1
 
3.1%

drat
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)67.7%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean3.58
Minimum2.76
Maximum4.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:22.042076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.76
5-th percentile2.845
Q13.08
median3.69
Q33.92
95-th percentile4.325
Maximum4.93
Range2.17
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation0.53510747
Coefficient of variation (CV)0.14947136
Kurtosis-0.34578398
Mean3.58
Median Absolute Deviation (MAD)0.48
Skewness0.37027896
Sum110.98
Variance0.28634
MonotonicityNot monotonic
2025-02-17T12:12:22.126486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.92 3
 
9.4%
3.07 3
 
9.4%
3.15 2
 
6.2%
4.08 2
 
6.2%
4.22 2
 
6.2%
3.9 2
 
6.2%
2.76 2
 
6.2%
3.08 2
 
6.2%
3.54 1
 
3.1%
4.43 1
 
3.1%
Other values (11) 11
34.4%
ValueCountFrequency (%)
2.76 2
6.2%
2.93 1
 
3.1%
3 1
 
3.1%
3.07 3
9.4%
3.08 2
6.2%
3.15 2
6.2%
3.21 1
 
3.1%
3.23 1
 
3.1%
3.54 1
 
3.1%
3.62 1
 
3.1%
ValueCountFrequency (%)
4.93 1
 
3.1%
4.43 1
 
3.1%
4.22 2
6.2%
4.08 2
6.2%
3.92 3
9.4%
3.9 2
6.2%
3.85 1
 
3.1%
3.77 1
 
3.1%
3.73 1
 
3.1%
3.7 1
 
3.1%

wt
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)90.3%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean3.2313548
Minimum1.513
Maximum5.424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:22.200110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.513
5-th percentile1.725
Q12.5425
median3.435
Q33.65
95-th percentile5.2975
Maximum5.424
Range3.911
Interquartile range (IQR)1.1075

Descriptive statistics

Standard deviation0.99131887
Coefficient of variation (CV)0.30678119
Kurtosis0.32674798
Mean3.2313548
Median Absolute Deviation (MAD)0.41
Skewness0.42137313
Sum100.172
Variance0.9827131
MonotonicityNot monotonic
2025-02-17T12:12:22.283509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3.44 3
 
9.4%
3.57 2
 
6.2%
2.14 1
 
3.1%
1.513 1
 
3.1%
2.77 1
 
3.1%
3.17 1
 
3.1%
2.62 1
 
3.1%
2.875 1
 
3.1%
2.32 1
 
3.1%
1.615 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
1.513 1
3.1%
1.615 1
3.1%
1.835 1
3.1%
1.935 1
3.1%
2.14 1
3.1%
2.2 1
3.1%
2.32 1
3.1%
2.465 1
3.1%
2.62 1
3.1%
2.77 1
3.1%
ValueCountFrequency (%)
5.424 1
3.1%
5.345 1
3.1%
5.25 1
3.1%
4.07 1
3.1%
3.845 1
3.1%
3.84 1
3.1%
3.78 1
3.1%
3.73 1
3.1%
3.57 2
6.2%
3.52 1
3.1%

qsec
Real number (ℝ)

High correlation  Missing 

Distinct27
Distinct (%)96.4%
Missing4
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean17.641786
Minimum14.5
Maximum22.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:22.358745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile14.8835
Q116.8275
median17.41
Q318.5425
95-th percentile20.0065
Maximum22.9
Range8.4
Interquartile range (IQR)1.715

Descriptive statistics

Standard deviation1.8039875
Coefficient of variation (CV)0.10225651
Kurtosis1.4879049
Mean17.641786
Median Absolute Deviation (MAD)0.92
Skewness0.69983741
Sum493.97
Variance3.2543708
MonotonicityNot monotonic
2025-02-17T12:12:22.439287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
17.02 2
 
6.2%
14.6 1
 
3.1%
16.7 1
 
3.1%
16.9 1
 
3.1%
15.5 1
 
3.1%
16.46 1
 
3.1%
14.5 1
 
3.1%
18.61 1
 
3.1%
18.52 1
 
3.1%
19.9 1
 
3.1%
Other values (17) 17
53.1%
(Missing) 4
 
12.5%
ValueCountFrequency (%)
14.5 1
3.1%
14.6 1
3.1%
15.41 1
3.1%
15.5 1
3.1%
15.84 1
3.1%
16.46 1
3.1%
16.7 1
3.1%
16.87 1
3.1%
16.9 1
3.1%
17.02 2
6.2%
ValueCountFrequency (%)
22.9 1
3.1%
20.01 1
3.1%
20 1
3.1%
19.9 1
3.1%
19.44 1
3.1%
18.9 1
3.1%
18.61 1
3.1%
18.52 1
3.1%
18.3 1
3.1%
18 1
3.1%

vs
Categorical

High correlation  Missing 

Distinct2
Distinct (%)6.5%
Missing1
Missing (%)3.1%
Memory size388.0 B
0.0
18 
1.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18
56.2%
1.0 13
40.6%
(Missing) 1
 
3.1%

Length

2025-02-17T12:12:22.518938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T12:12:22.598893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18
58.1%
1.0 13
41.9%

Most occurring characters

ValueCountFrequency (%)
0 49
52.7%
. 31
33.3%
1 13
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49
52.7%
. 31
33.3%
1 13
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49
52.7%
. 31
33.3%
1 13
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49
52.7%
. 31
33.3%
1 13
 
14.0%

am
Categorical

High correlation  Missing 

Distinct2
Distinct (%)6.5%
Missing1
Missing (%)3.1%
Memory size388.0 B
0.0
19 
1.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 19
59.4%
1.0 12
37.5%
(Missing) 1
 
3.1%

Length

2025-02-17T12:12:22.707105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T12:12:22.757219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19
61.3%
1.0 12
38.7%

Most occurring characters

ValueCountFrequency (%)
0 50
53.8%
. 31
33.3%
1 12
 
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
53.8%
. 31
33.3%
1 12
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
53.8%
. 31
33.3%
1 12
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
53.8%
. 31
33.3%
1 12
 
12.9%

carb
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.78125
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:22.803279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median2
Q34
95-th percentile4.9
Maximum8
Range7
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.6408962
Coefficient of variation (CV)0.58998515
Kurtosis1.8442974
Mean2.78125
Median Absolute Deviation (MAD)1
Skewness1.1223332
Sum89
Variance2.6925403
MonotonicityNot monotonic
2025-02-17T12:12:23.651348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 10
31.2%
2 9
28.1%
1 8
25.0%
3 3
 
9.4%
8 1
 
3.1%
6 1
 
3.1%
ValueCountFrequency (%)
1 8
25.0%
2 9
28.1%
3 3
 
9.4%
4 10
31.2%
6 1
 
3.1%
8 1
 
3.1%
ValueCountFrequency (%)
8 1
 
3.1%
6 1
 
3.1%
4 10
31.2%
3 3
 
9.4%
2 9
28.1%
1 8
25.0%

power
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)73.3%
Missing2
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean522.83333
Minimum4
Maximum1675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.0 B
2025-02-17T12:12:23.731013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile226
Q1318.75
median473.5
Q3558.75
95-th percentile1119.75
Maximum1675
Range1671
Interquartile range (IQR)240

Descriptive statistics

Standard deviation324.68436
Coefficient of variation (CV)0.6210093
Kurtosis5.4228624
Mean522.83333
Median Absolute Deviation (MAD)151
Skewness1.9716718
Sum15685
Variance105419.94
MonotonicityNot monotonic
2025-02-17T12:12:23.858830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
540 3
 
9.4%
450 2
 
6.2%
440 2
 
6.2%
492 2
 
6.2%
264 2
 
6.2%
735 2
 
6.2%
525 2
 
6.2%
1320 1
 
3.1%
372 1
 
3.1%
875 1
 
3.1%
Other values (12) 12
37.5%
(Missing) 2
 
6.2%
ValueCountFrequency (%)
4 1
3.1%
208 1
3.1%
248 1
3.1%
260 1
3.1%
264 2
6.2%
291 1
3.1%
315 1
3.1%
330 1
3.1%
372 1
3.1%
440 2
6.2%
ValueCountFrequency (%)
1675 1
 
3.1%
1320 1
 
3.1%
875 1
 
3.1%
735 2
6.2%
690 1
 
3.1%
645 1
 
3.1%
565 1
 
3.1%
540 3
9.4%
525 2
6.2%
492 2
6.2%

Interactions

2025-02-17T12:12:19.354794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:03.330710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:11.826281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.062651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.801916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.369014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.099193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.679432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.457738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:05.032735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:12.037881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.136590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.880562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.442114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.155525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.754906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.534794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:06.102304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:12.621203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.202881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.967570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.525408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.228509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.826618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.609322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:06.300297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:14.732059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.300139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.025641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.641717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.292994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.912004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.673590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:06.558259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:15.742801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.381683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.087589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.723830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.359094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.021690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.787056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:08.294814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:15.835022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.465497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.163640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.833157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.448059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.141575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.859196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:09.280733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:15.921566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.562995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.225455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.932198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.513701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.208378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.934355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:11.247902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:15.994677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:16.697353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:17.292903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.021222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:18.583126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T12:12:19.280654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-17T12:12:23.938105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amcarbcyldispdratgearhpmpgpowerqsecvswt
am1.0000.2100.4330.3710.6650.7790.2960.3870.3140.3800.0000.674
carb0.2101.0000.4770.544-0.1040.2180.730-0.6450.778-0.5630.5820.492
cyl0.4330.4771.0000.7670.4900.4720.7690.7070.4990.5040.7790.636
disp0.3710.5440.7671.000-0.6710.5650.852-0.9010.702-0.3890.5660.901
drat0.665-0.1040.490-0.6711.0000.718-0.4790.649-0.3010.0700.399-0.752
gear0.7790.2180.4720.5650.7181.0000.6320.3920.6820.4400.5500.272
hp0.2960.7300.7690.852-0.4790.6321.000-0.8850.936-0.5890.6850.766
mpg0.387-0.6450.707-0.9010.6490.392-0.8851.000-0.7250.4000.618-0.884
power0.3140.7780.4990.702-0.3010.6820.936-0.7251.000-0.6970.6420.576
qsec0.380-0.5630.504-0.3890.0700.440-0.5890.400-0.6971.0000.664-0.136
vs0.0000.5820.7790.5660.3990.5500.6850.6180.6420.6641.0000.580
wt0.6740.4920.6360.901-0.7520.2720.766-0.8840.576-0.1360.5801.000

Missing values

2025-02-17T12:12:20.054919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-17T12:12:20.182377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-17T12:12:20.417697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

gearmodelmpgcyldisphpdratwtqsecvsamcarbpower
05Maserati Bora15.08.0301.0335.03.543.57014.600.01.081675.0
15Porsche 914-226.04.0120.391.04.432.14016.700.01.02455.0
25Ferrari Dino19.76.0145.0175.03.622.77015.500.01.06875.0
35Lotus Europa30.44.095.1113.03.771.51316.901.01.02565.0
45Ford Pantera L15.88.0351.0264.04.223.17014.500.01.041320.0
54Mazda RX421.06.0160.0110.03.902.62016.460.01.04440.0
64Datsun 71022.84.0108.093.03.852.32018.611.01.01372.0
74Mazda RX4 Wag21.06.0160.0110.03.902.87517.020.01.04440.0
84NaN30.44.075.752.04.931.61518.521.01.02208.0
94NaN33.94.071.165.04.221.83519.901.01.01260.0
gearmodelmpgcyldisphpdratwtqsecvsamcarbpower
223Valiant18.16.0225.0105.02.763.460NaN1.00.01315.0
233Duster 36014.38.0360.0245.03.213.57015.840.00.04735.0
243Hornet 4 Drive21.46.0258.0110.03.083.21519.441.00.01330.0
253Camaro Z2813.38.0350.0245.03.733.84015.410.00.04735.0
263NaN15.58.0318.0150.02.763.52016.870.00.02450.0
273NaN21.54.0120.197.03.702.46520.011.00.01291.0
283Chrysler Imperial14.78.0440.0230.03.235.34517.420.00.04690.0
293AMC Javelin15.28.0304.0150.03.153.43517.300.00.02450.0
303Pontiac Firebird19.28.0400.0175.03.083.84517.050.00.02525.0
312VolvoNaNNaN4.02.0NaNNaNNaNNaNNaN14.0